In the field of image-based pattern recognition, an image to be recognized is collected by a sensor, and if the image has poor resolution, accuracy of recognition results greatly decreases, particularly for recognition among confusing recognition objects, such as “O” and “D”, etc, in character recognition. Though computer technology is developing rapidly, in consideration of cost, a hardware platform only satisfying actual need instead of a high-end hardware platform may be selected. In this case, in practical manufacturing process, due to differences of hardware and diversity of recognition space, a lot of recognition errors for confusing objects may occur.
There are usually two existing solutions. One is to update hardware devices to improve image resolution, and the other one is to improve a recognition algorithm to improve recognition accuracy. In the first solution, the problem of recognition errors for confusing objects can be completely solved, but in this way resolution of the entire image is improved and accordingly information collecting device, storage device and processor, etc, need to be updated, which greatly increases production cost and weakens product competitiveness. In the second solution, signal reconstruction technology is used for processing the collected signal, to improve a ratio between useful signal data volume to noise data volume and improve signal quality. However, in the actually collected signal, the useful signal data volume is much larger than the noise data volume, and in this case reconstructing all collected signals causes large amount of data processing and slow algorithm processing speed. Therefore, this solution is not ideal even though the recognition accuracy may be improved to certain extent.
Hence, it is very important to provide an image-based character recognition method with fast processing speed and high recognition accuracy in the technical field of sheet-medium recognition and classification.